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Record W4291923145 · doi:10.1016/j.jecp.2022.105518

Alice in Wonderland: The effects of body size and movement on children’s size perception and body representation in virtual reality

2022· article· en· W4291923145 on OpenAlexafffund
Samantha Keenaghan, Marie Polášková, Simon Thurlbeck, Robert W. Kentridge, Dorothy Cowie

Bibliographic record

VenueJournal of Experimental Child Psychology · 2022
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsCanadian Institute for Advanced Research
FundersEconomic and Social Research CouncilCanadian Institutes of Health ResearchCanadian Institute for Advanced Research
KeywordsAvatarEmbodied cognitionPsychologyIllusionPerceptionPerspective (graphical)Body schemaVirtual representationFeelingBody shapeRepresentation (politics)Cognitive psychologyVirtual realityDevelopmental psychologySocial psychologyHuman–computer interactionComputer science

Abstract

fetched live from OpenAlex

Previous work shows that in adults, illusory embodiment of a virtual avatar can be induced using congruent visuomotor cues. Furthermore, embodying different-sized avatars influences adults' perception of their environment's size. This study (N = 92) investigated whether children are also susceptible to such embodiment and size illusions. Adults and 5-year-old children viewed a first-person perspective of different-sized avatars moving either congruently or incongruently with their own body. Participants rated their feelings of embodiment over the avatar and also estimated the sizes of their body and objects in the environment. Unlike adults, children embodied the avatar regardless of visuomotor congruency. Both adults and children freely embodied different-sized avatars, and this affected their size perception in the surrounding virtual environment; they felt that objects were larger in a small body and vice versa in a large body. In addition, children felt that their body had grown in the large body condition. These findings have important implications for both our theoretical understanding of own-body representation, and our knowledge of perception in virtual environments.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.939
Threshold uncertainty score0.333

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.329
Teacher spread0.316 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2022
Admission routes2
Has abstractyes

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